Update app.py

app.py

@@ -24,6 +24,9 @@ from itertools import combinations
 import pypdf
 import requests
 
+from chromadb.utils import embedding_functions
+from chromadb import Documents, EmbeddingFunction, Embeddings
+
 # LLM: openai and google_genai
 import openai
 from langchain_openai import OpenAI, OpenAIEmbeddings, ChatOpenAI
@@ -84,6 +87,14 @@ cohere_api_key = os.environ['cohere_api']
 
 current_dir = os.getcwd()
 
+# for new Chromadb   
+class MyEmbeddingFunction(EmbeddingFunction[Documents]):
+    def __call__(self, input: Documents) -> Embeddings:
+        sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="BAAI/bge-large-en-v1.5")
+        embeddings = sentence_transformer_ef(input)
+        return embeddings
+custom = MyEmbeddingFunction()
+
 
 
 prompt_templates = {"All Needs Experts": "Respond as if you are combination of all needs assessment experts."}
@@ -244,7 +255,8 @@ embeddings_HuggingFace = select_embeddings_model(LLM_service="HuggingFace")
 def create_vectorstore(embeddings,documents,vectorstore_name):
     """Create a Chroma vector database."""
     persist_directory = (current_dir + "/" + vectorstore_name)
-    embedding_function=embeddings
+    #embedding_function=embeddings
+    embedding_function=custom
     vector_store = Chroma.from_documents(
         documents=documents,
         embedding=embeddings,